In the Beginning: Biobanking Strategy and Study Design for Chronic Obstructive Pulmonary Disease Research

When setting out on a large-scale biomarker discovery study, it is important to factor biobanking needs into the study design right from the start. Truedsson et al. (2016) describe how a collaborative study, KOL-Örestad, between three Swedish health care institutions incorporated good biobanking practices to ensure data quality and consistency from the start.¹

Chronic obstructive pulmonary disease (COPD) is currently the third-greatest killer worldwide. It is irreversibly progressive, resulting in symptoms due to chronic bronchitis, lung tissue destruction and airflow limitation. Seen mostly in smokers, COPD is also associated with lung cancer and cardiovascular disease. Researchers do not fully understand the pathological progression of the disease, and although 15% of smokers develop COPD, there are no effective diagnostic biomarkers that correlate well with predictive risk or clinical stage. Likewise, lung function tests, such as forced expiratory volume, which are difficult to obtain and not prognostic, especially in early stages, are poor indicators.

In collaboration with Örestadskliniken, a primary health care clinic in Malmö, researchers at Skåne University Hospital, Malmö, and the Center of Excellence in Biological and Medical Mass Spectrometry (CEBMMS) at the University of Lund are aiming to identify biomarkers to facilitate early diagnosis of and treatment for patients with COPD.

Staff at the primary clinic will recruit participants from among three patient populations:

1. Smokers and ex-smokers with confirmed COPD according to current clinical standards (airway outflow restriction, in addition to satisfying Global Initiative for Chronic Obstructive Lung Disease [GOLD] diagnostic criteria for stages 1–4)
2. Healthy individuals who have never smoked
3. Healthy smokers or ex-smokers without COPD

Once enrolled, participants undergo a full medical examination, including lung function testing; submit their medical history; and answer a quality of life questionnaire. At this point, clinical staff collect blood samples, which then enter the biobanking sample processing workflow. Participants return for further data and biosample collection every six months for the duration of the study (five years). Patients with COPD revisit more frequently since the study protocol requires further biosampling when symptoms worsen. Exclusions for enrollment include existing chronic inflammatory disease, and treatment with steroids or immunomodulators other than for COPD.

Prior to starting this longitudinal study, researchers obtained full ethics approval from the appropriate review boards. They also established availability of suitable biobanking facilities and protocols for large-scale data acquisition to maintain data value and consistency. For this function, the study designers chose CEBMMS, which has established a robust framework and workflows for handling mass spectrometric evaluations in large populations.

Biosampling

The blood samples collected at the primary health care facility contribute to plasma, buffy coat, erythrocyte, serum and whole blood fractions stored at −80°C. Once collected, a courier company brings the samples to the CEBMMS biobank for processing and storage. The facility uses automation to divide samples into multiple aliquots, using a robotic system to distribute volumes among 384-well plates for high-density storage. The process includes barcoding vials and aliquots with an encrypted system that preserves patient anonymity while allowing efficient automated storage and retrieval for subsequent research. The whole process from collection to storage takes less than two hours.

Clinical database

The study design employs barcode labeling to link all patient records, biosamples and results electronically. A flexible platform, REDCap, allows user-specified input while controlling individual access parameters. There is also functionality for clinicians to follow patients within the system and order additional testing and clinical care.

Data acquisition for biomarker discovery

The study will use an established quantitative proteomics workflow for biomarker discovery, in addition to gaining supplementary data from traditional clinical chemistry. Researchers analyze samples with multiple reaction monitoring liquid chromatography–tandem mass spectrometry (MRM LC-MS/MS) assays run at CEBMMS. They will base protein identifications on a library established following a Proteome Discoverer (Thermo Scientific) database search.

Since starting in 2014, the study has recruited 200 individuals. Truedsson et al. state that the study design will allow researchers to follow individual patients though disease progression. In addition, they will be able to explore the impact of other allied disease states, such as lung cancer and cardiovascular disease. The research team is confident that the biobanking methodology described will enable and support novel diagnostic, predictive and prognostic biomarker discovery in early COPD.

Reference
1. Truedsson, M., et al. (2016) “Biomarkers of early chronic obstructive pulmonary disease (COPD) in smokers and former smokers. Protocol of a longitudinal study,” Clinical and Translational Medicine, 5(9), doi: 10.1186/s40169-016-0086-5.